Spring 2025: Software Engineering Lab

Instructions

• Please be on time to avoid the Attendance Penalty.
• Please put your mobile phone in the Silent Mode.
• Each lab assignment needs to be submitted in the Google Classroom for evaluation(will be notified in the GC lab-wise, submit before the deadline).
• Turn off(shut down) your assigned computer and arrange the chair before you leave the lab.

Guidelines

• As per DUCS guidelines DSE: Data Mining

Lab 0: Getting Started ( week of 05^th & 12^th August 2024 )

Q. NO.	Program	Practical No.	Remarks
1	https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial1/tutorial1.html	Practice Set No. 1	Introduction to Python
2	https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial2/tutorial2.html	Practice Set No. 2	Introduction to Numpy and Pandas
3	https://www.cse.msu.edu/~ptan/dmbook/tutorials/tutorial3/tutorial3.html	Practice Set No. 3	Data Exploration

Lab 1: ( week of 19^th & 26^th August 2024 )

Q. NO.	Program	Practical No.	Remarks
1	Apply data cleaning techniques on any dataset (e.g. Chronic Kidney Disease dataset from UCI repository). Techniques may include handling missing values, outliers and inconsistent values. Also, a set of validation rules may be specified for the particular dataset and validation checks performed.	Practical No. 1	Dataset: kidneyDisease.csv Download from Kaggle: Chronic KIdney Disease dataset Tutorial: Tutorial on Handling Missing values

Lab 2: ( week of 2^nd & 9^th September 2024 )

Q. NO.	Program	Practical No.	Remarks
1	Apply data pre-processing techniques such as standardization/normalization, transformation, aggregation, discretization/binarization, sampling etc. on any dataset	Practical No. 2	Dataset: rain.csv Download from data.gov.in: Rainfall in India

Lab 3: ( week of 16^th, 23^rd & 30^thSeptember 2024 )

Q. NO.	Program	Practical No.	Remarks
1	Writing/Review of Chapter 1, Chapter 3, and Chapter 4 of Project Report	Project Work

Lab 4: ( week of 7^th October 2024 )

Q. NO.	Program	Practical No.	Remarks
1	Apply simple K-means algorithm for clustering any dataset. Compare the performance of clusters by varying the algorithm parameters. For a given set of parameters, plot a line graph depicting MSE obtained after each iteration.	Practical No. 3	Dataset: Mall_Customers.csv Download from data from kaggle: Mall Customer Segmentation Data

Projects

Team No.	Project Title	Team Members	Outcomes/Remarks
1	Title 1	Name (RollNo.) Name (RollNo.) Name (RollNo.) Name (RollNo.)	Report: Project Presentation:
2	Title 2	Name (RollNo.) Name (RollNo.) Name (RollNo.) Name (RollNo.)	Report: Project Presentation: